ADAPTIVE ESTIMATION OF REGRESSION-MODELS VIA MOMENT RESTRICTIONS
ADAPTIVE ESTIMATION OF REGRESSION-MODELS VIA MOMENT RESTRICTIONS
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DOI：
10.1016/0304-4076(88)90048-6
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发表时间：
1988-07-01
影响因子：
6.3
通讯作者：
NEWEY, WK
中科院分区：
文献类型：
--
作者：
NEWEY, WK
本文利用广义矩估计方法研究回归模型的自适应估计。考虑两种模型，一种具有i.i.d.。独立于回归变量的扰动和具有条件对称但（可能）异方差扰动的扰动。对于这两种情况下，本文开发的线性估计，是渐近有效的，如果数量和各种矩条件允许增长以适当的速度与样本量。在一般对称的情况下，没有其他自适应估计尚未提出。此外，一个小的Monte Carlo研究的结果表明，在独立的情况下，小样本性能的广义矩估计方法可以很好的相对于其他估计先前提出的。
This paper considers adaptive estimation of regression models by means of generalized method of moments estimators. Two models are considered, one with an i.i.d. disturbance that is independent of the regressors and one with a conditionally symmetric but (possibly) heteroskedastic disturbance. For both cases the paper develops linearized estimators that are asymptotically efficient if the number and variety of moment conditions are allowed to grow at an appropriate rate with the sample size. In the general symmetric case no other adaptive estimator has yet been proposed. Also, results of a small Monte Carlo study indicate that in the independence case the small sample performance of the generalized method of moments estimator can be quite good vis-a-vis other estimators previously proposed.